A data element forms the premise on which an inference may be drawn and represents the lowest level of abstraction from which information and then knowledge are derived. In humans, the perception of environment or condition is comprised of data gathered by the senses, i.e., the physiological capacity to provide input for perception. These “senses” are formally referred to as the exteroceptive senses and in humans comprise quantifiable or potential sensory data including, sight, smell, hearing, touch, taste, temperature, pressure, pain, and pleasure, the admixture of which determine the spectrum of human emotion states and resultant behaviors.
Potentials in these senses work independently, or in combination, to produce unique perceptions. For instance, the sense of sight is primarily used to identify a food item, but the flavor of the food item incorporates the senses of both taste and smell.
In biological terms, behavior can generally be regarded as any action of an organism that changes its relationship to its environment. Definable and measurable behaviors are predicated on the association of stimuli within the domain of exteroceptive sensation, to perception, and ultimately, a behavioral outcome.
The ability to determine the exteroceptive association and impact on behavior from data that is not physical but exists only in digital form has profound implications for how data is viewed, both intrinsically and associatively.
An advantage exists, therefore, for a system and method for dynamically associating digital data with values that approximate exteroceptive stimuli potentials, and from those values forecasting probabilistically the likely behavioral response to that data, thereby promoting the ability to design systems and models to predict behavioral outcomes that are inherently more accurate in determining behavioral response. In turn, interfaces and computing devices may be developed that would “expect” certain behaviors, or illicit them through the manipulation of data. Additionally, models could be constructed to classify data not only for the intrinsic value of the data but for the potential behavioral influence inherent in the data as well.